Abstract: In this paper, we present a new pipeline which automatically identifies and
annotates axoplasmic reticula, which are small subcellular structures present
only in axons. We run our algorithm on the Kasthuri11 dataset, which was color
corrected using gradient-domain techniques to adjust contrast. We use a
bilateral filter to smooth out the noise in this data while preserving edges,
which highlights axoplasmic reticula. These axoplasmic reticula are then
annotated using a morphological region growing algorithm. Additionally, we
perform Laplacian sharpening on the bilaterally filtered data to enhance edges,
and repeat the morphological region growing algorithm to annotate more
axoplasmic reticula. We track our annotations through the slices to improve
precision, and to create long objects to aid in segment merging. This method
annotates axoplasmic reticula with high precision. Our algorithm can easily be
adapted to annotate axoplasmic reticula in different sets of brain data by
changing a few thresholds. The contribution of this work is the introduction of
a straightforward and robust pipeline which annotates axoplasmic reticula with
high precision, contributing towards advancements in automatic feature
annotations in neural EM data.